For the curious you can see the dictionary here: https://github.com/sloria/TextBlob/blob/eb08c120d364e9086467...
The package used is a pretty popular one called TextBlob. It is nifty for working with unlabeled data like we have with the HackerNews dataset.
We really focused our definition of saltiness around being a combination of (subjective + negative) comments.
We reduced the impact of (objective + negative) as we feel that criticism, while at times painful, if presented objectively isn't necessarily salty.
We built this model fast (1 week) and have since iterated this week into developing a Fine Tuned BERT model that we are training over a much broader set of toxicity, demographic, and polarity features. The training set is much larger and higher quality so we are expecting a large jump in precision upon deployment.
I hope the app gave you some good chuckles as you went around though. It's hard to explain how excited I felt when I saw pg_is_a_butt at the top of my pandas data frame the first time I processed the data.
It's doing a little bit right. :)
This kind of feedback gets me pumped about continuing to work on it.
Thank you for trying the app and hopefully V2 will leave you feeling like the system is more precise.
It certainly does; although it's possible they aren't stored independently and simply "cancel out" an upvote, so maybe that's what you meant.
The interface and the graphs are really nice; even though basing the data on votes alone would be less interesting in one sense, I think the rest of the site would still provide value even with that simpler metric.
I have written posts very much saltier than the ones scored as saltiest by this ranking algorithm, possibly because I didn't use inherently negative vocabulary to express a highly negative sentiment.
It's a fun party trick, but its usefulness is limited without semantic analysis or live-human scoring.
20.23% of my posts are rated as "salty". I wonder what percentage of scoring words are rated as negative.